The effective throughput of a network can be improved, for example, using data compression. Data compression involves encoding information using fewer bits than the original compression. Data may be compressed, transmitted, and then decompressed. Compression reduces data storage space and transmission capacity, with a tradeoff of increased computation.
Data compression may be lossy or lossless. In lossless data compression, statistical redundancy is identified and eliminated, and no information is lost. Examples of lossless compression include Lempel-Ziv (LZ) compression, DEFLATE compression, LZ-Renau compression, Huffman coding, compression based on probabilistic models such as prediction by partial matching, grammar-based coding, and arithmetic coding. In lossy compression, marginally important information is identified and removed. Lossy data compression schemes are based on how people perceive the data in question. For example, the human eye is more sensitive to subtle variations in luminance than the variations in color. Examples of lossy compression include JPEG compression, MPEG compression, and Mp3 compression. Different coding methods are more efficient in compressing different data types. For example, JPEG compression is best used to compress images, MPEG compression is best used to compress video, MP3 compression is best used to compress audio, a lossless compression scheme is best used to compress a text file, and no compression is best for an already compressed file.